{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2019162542\\t/front-api/bill/create\\t8\\t1057.31...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644\\t/front-api/bill/create\\t5\\t749.12\\t103...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742\\t/front-api/bill/create\\t5\\t845.84\\t136...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808\\t/front-api/bill/create\\t9\\t1305.52\\t90...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943\\t/front-api/bill/create\\t3\\t568.89\\t138...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "                                                   0\n",
       "0  2019162542\\t/front-api/bill/create\\t8\\t1057.31...\n",
       "1  162644\\t/front-api/bill/create\\t5\\t749.12\\t103...\n",
       "2  162742\\t/front-api/bill/create\\t5\\t845.84\\t136...\n",
       "3  162808\\t/front-api/bill/create\\t9\\t1305.52\\t90...\n",
       "4  162943\\t/front-api/bill/create\\t3\\t568.89\\t138..."
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt', header = None)\n",
    "# 默认读取前5条\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0                       1  2        3       4       5      6   7  \\\n",
       "0  2019162542  /front-api/bill/create  8  1057.31   88.75  177.72  132.0  60   \n",
       "1      162644  /front-api/bill/create  5   749.12  103.79  240.38  149.0  60   \n",
       "2      162742  /front-api/bill/create  5   845.84  136.31  225.73  169.0  60   \n",
       "3      162808  /front-api/bill/create  9  1305.52   90.12  196.61  145.0  60   \n",
       "4      162943  /front-api/bill/create  3   568.89  138.45  232.02  189.0  60   \n",
       "\n",
       "                     8  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./log.txt', header = None, sep = '\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
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       "      <th>interval</th>\n",
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       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
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       "      <td>177.72</td>\n",
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       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3      162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4      162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['id', 'api', 'count', 'res_time_sum', 'res_time_min', 'res_time_max', 'res_time_avg', 'interval', 'created_at']\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179496, 9)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
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       "      <td>240.38</td>\n",
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       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "2      162742      5        845.84        136.31        225.73         169.0   \n",
       "3      162808      9       1305.52         90.12        196.61         145.0   \n",
       "4      162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "   interval           created_at  \n",
       "0        60  2018-11-01 00:00:07  \n",
       "1        60  2018-11-01 00:01:07  \n",
       "2        60  2018-11-01 00:02:07  \n",
       "3        60  2018-11-01 00:03:07  \n",
       "4        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# api这一列都一样，没什么分析价值，删除\n",
    "df = df.drop('api', axis = 1)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-05-26 20:23:17\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100191</th>\n",
       "      <td>7434210</td>\n",
       "      <td>9</td>\n",
       "      <td>2750.62</td>\n",
       "      <td>108.90</td>\n",
       "      <td>1243.21</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:00:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100192</th>\n",
       "      <td>7434303</td>\n",
       "      <td>10</td>\n",
       "      <td>2680.90</td>\n",
       "      <td>109.79</td>\n",
       "      <td>621.66</td>\n",
       "      <td>268.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:01:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100193</th>\n",
       "      <td>7434351</td>\n",
       "      <td>3</td>\n",
       "      <td>375.49</td>\n",
       "      <td>69.80</td>\n",
       "      <td>158.44</td>\n",
       "      <td>125.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:02:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100194</th>\n",
       "      <td>7434477</td>\n",
       "      <td>1</td>\n",
       "      <td>133.46</td>\n",
       "      <td>133.46</td>\n",
       "      <td>133.46</td>\n",
       "      <td>133.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:04:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100195</th>\n",
       "      <td>7434554</td>\n",
       "      <td>4</td>\n",
       "      <td>431.09</td>\n",
       "      <td>76.27</td>\n",
       "      <td>130.19</td>\n",
       "      <td>107.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:05:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100196</th>\n",
       "      <td>7434607</td>\n",
       "      <td>3</td>\n",
       "      <td>838.02</td>\n",
       "      <td>214.77</td>\n",
       "      <td>349.92</td>\n",
       "      <td>279.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:06:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100197</th>\n",
       "      <td>7434641</td>\n",
       "      <td>2</td>\n",
       "      <td>348.50</td>\n",
       "      <td>109.39</td>\n",
       "      <td>239.11</td>\n",
       "      <td>174.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:07:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100198</th>\n",
       "      <td>7434748</td>\n",
       "      <td>1</td>\n",
       "      <td>98.17</td>\n",
       "      <td>98.17</td>\n",
       "      <td>98.17</td>\n",
       "      <td>98.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:08:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100199</th>\n",
       "      <td>7434769</td>\n",
       "      <td>2</td>\n",
       "      <td>320.97</td>\n",
       "      <td>150.27</td>\n",
       "      <td>170.70</td>\n",
       "      <td>160.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:09:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100200</th>\n",
       "      <td>7434885</td>\n",
       "      <td>2</td>\n",
       "      <td>460.92</td>\n",
       "      <td>124.20</td>\n",
       "      <td>336.72</td>\n",
       "      <td>230.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:10:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100201</th>\n",
       "      <td>7434925</td>\n",
       "      <td>5</td>\n",
       "      <td>778.83</td>\n",
       "      <td>114.60</td>\n",
       "      <td>218.03</td>\n",
       "      <td>155.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:11:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100202</th>\n",
       "      <td>7434985</td>\n",
       "      <td>5</td>\n",
       "      <td>1187.60</td>\n",
       "      <td>148.29</td>\n",
       "      <td>483.77</td>\n",
       "      <td>237.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:12:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100203</th>\n",
       "      <td>7435027</td>\n",
       "      <td>8</td>\n",
       "      <td>1327.02</td>\n",
       "      <td>64.50</td>\n",
       "      <td>494.33</td>\n",
       "      <td>165.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:13:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100204</th>\n",
       "      <td>7435113</td>\n",
       "      <td>3</td>\n",
       "      <td>693.93</td>\n",
       "      <td>122.96</td>\n",
       "      <td>411.39</td>\n",
       "      <td>231.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:14:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100205</th>\n",
       "      <td>7435146</td>\n",
       "      <td>1</td>\n",
       "      <td>222.28</td>\n",
       "      <td>222.28</td>\n",
       "      <td>222.28</td>\n",
       "      <td>222.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:15:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100206</th>\n",
       "      <td>7435216</td>\n",
       "      <td>1</td>\n",
       "      <td>187.44</td>\n",
       "      <td>187.44</td>\n",
       "      <td>187.44</td>\n",
       "      <td>187.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:16:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100207</th>\n",
       "      <td>7435317</td>\n",
       "      <td>1</td>\n",
       "      <td>150.75</td>\n",
       "      <td>150.75</td>\n",
       "      <td>150.75</td>\n",
       "      <td>150.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:17:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100208</th>\n",
       "      <td>7435342</td>\n",
       "      <td>6</td>\n",
       "      <td>1023.25</td>\n",
       "      <td>104.30</td>\n",
       "      <td>327.92</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:18:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100209</th>\n",
       "      <td>7435499</td>\n",
       "      <td>1</td>\n",
       "      <td>305.95</td>\n",
       "      <td>305.95</td>\n",
       "      <td>305.95</td>\n",
       "      <td>305.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:20:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100210</th>\n",
       "      <td>7435518</td>\n",
       "      <td>3</td>\n",
       "      <td>435.64</td>\n",
       "      <td>125.27</td>\n",
       "      <td>168.81</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:21:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100211</th>\n",
       "      <td>7435590</td>\n",
       "      <td>4</td>\n",
       "      <td>1013.84</td>\n",
       "      <td>80.00</td>\n",
       "      <td>693.16</td>\n",
       "      <td>253.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:22:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100212</th>\n",
       "      <td>7435671</td>\n",
       "      <td>2</td>\n",
       "      <td>439.20</td>\n",
       "      <td>103.40</td>\n",
       "      <td>335.80</td>\n",
       "      <td>219.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:23:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100213</th>\n",
       "      <td>7435754</td>\n",
       "      <td>1</td>\n",
       "      <td>101.32</td>\n",
       "      <td>101.32</td>\n",
       "      <td>101.32</td>\n",
       "      <td>101.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:24:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100214</th>\n",
       "      <td>7435765</td>\n",
       "      <td>5</td>\n",
       "      <td>710.37</td>\n",
       "      <td>92.82</td>\n",
       "      <td>187.95</td>\n",
       "      <td>142.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:25:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100215</th>\n",
       "      <td>7435822</td>\n",
       "      <td>6</td>\n",
       "      <td>893.37</td>\n",
       "      <td>103.36</td>\n",
       "      <td>219.25</td>\n",
       "      <td>148.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:26:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100216</th>\n",
       "      <td>7435923</td>\n",
       "      <td>2</td>\n",
       "      <td>223.93</td>\n",
       "      <td>104.11</td>\n",
       "      <td>119.82</td>\n",
       "      <td>111.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:27:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100217</th>\n",
       "      <td>7435990</td>\n",
       "      <td>1</td>\n",
       "      <td>200.50</td>\n",
       "      <td>200.50</td>\n",
       "      <td>200.50</td>\n",
       "      <td>200.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:28:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100218</th>\n",
       "      <td>7436073</td>\n",
       "      <td>2</td>\n",
       "      <td>447.68</td>\n",
       "      <td>101.22</td>\n",
       "      <td>346.46</td>\n",
       "      <td>223.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:30:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100219</th>\n",
       "      <td>7436124</td>\n",
       "      <td>4</td>\n",
       "      <td>1351.68</td>\n",
       "      <td>116.12</td>\n",
       "      <td>570.62</td>\n",
       "      <td>337.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:31:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100220</th>\n",
       "      <td>7436217</td>\n",
       "      <td>2</td>\n",
       "      <td>336.21</td>\n",
       "      <td>165.75</td>\n",
       "      <td>170.46</td>\n",
       "      <td>168.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-01 00:32:41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107762</th>\n",
       "      <td>7975946</td>\n",
       "      <td>12</td>\n",
       "      <td>2352.38</td>\n",
       "      <td>75.99</td>\n",
       "      <td>396.05</td>\n",
       "      <td>196.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:30:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107763</th>\n",
       "      <td>7976014</td>\n",
       "      <td>8</td>\n",
       "      <td>1229.78</td>\n",
       "      <td>55.08</td>\n",
       "      <td>272.57</td>\n",
       "      <td>153.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:31:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107764</th>\n",
       "      <td>7976098</td>\n",
       "      <td>5</td>\n",
       "      <td>783.28</td>\n",
       "      <td>71.80</td>\n",
       "      <td>287.61</td>\n",
       "      <td>156.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:32:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107765</th>\n",
       "      <td>7976112</td>\n",
       "      <td>11</td>\n",
       "      <td>1692.83</td>\n",
       "      <td>72.39</td>\n",
       "      <td>340.03</td>\n",
       "      <td>153.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:33:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107766</th>\n",
       "      <td>7976220</td>\n",
       "      <td>10</td>\n",
       "      <td>2142.50</td>\n",
       "      <td>88.62</td>\n",
       "      <td>336.89</td>\n",
       "      <td>214.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:34:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107767</th>\n",
       "      <td>7976272</td>\n",
       "      <td>11</td>\n",
       "      <td>1696.39</td>\n",
       "      <td>66.28</td>\n",
       "      <td>299.57</td>\n",
       "      <td>154.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:35:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107768</th>\n",
       "      <td>7976387</td>\n",
       "      <td>5</td>\n",
       "      <td>787.14</td>\n",
       "      <td>91.22</td>\n",
       "      <td>356.16</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:36:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107769</th>\n",
       "      <td>7976439</td>\n",
       "      <td>9</td>\n",
       "      <td>1311.06</td>\n",
       "      <td>73.65</td>\n",
       "      <td>221.67</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:37:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107770</th>\n",
       "      <td>7976528</td>\n",
       "      <td>11</td>\n",
       "      <td>1446.02</td>\n",
       "      <td>89.36</td>\n",
       "      <td>191.83</td>\n",
       "      <td>131.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:38:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107771</th>\n",
       "      <td>7976580</td>\n",
       "      <td>9</td>\n",
       "      <td>1425.64</td>\n",
       "      <td>74.92</td>\n",
       "      <td>283.87</td>\n",
       "      <td>158.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:39:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107772</th>\n",
       "      <td>7976654</td>\n",
       "      <td>6</td>\n",
       "      <td>1207.68</td>\n",
       "      <td>82.50</td>\n",
       "      <td>327.82</td>\n",
       "      <td>201.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:40:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107773</th>\n",
       "      <td>7976734</td>\n",
       "      <td>5</td>\n",
       "      <td>858.62</td>\n",
       "      <td>82.31</td>\n",
       "      <td>280.92</td>\n",
       "      <td>171.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:41:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107774</th>\n",
       "      <td>7976823</td>\n",
       "      <td>4</td>\n",
       "      <td>622.49</td>\n",
       "      <td>91.27</td>\n",
       "      <td>212.77</td>\n",
       "      <td>155.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:42:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107775</th>\n",
       "      <td>7976860</td>\n",
       "      <td>5</td>\n",
       "      <td>992.69</td>\n",
       "      <td>75.42</td>\n",
       "      <td>353.22</td>\n",
       "      <td>198.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:43:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107776</th>\n",
       "      <td>7976961</td>\n",
       "      <td>9</td>\n",
       "      <td>1411.06</td>\n",
       "      <td>102.67</td>\n",
       "      <td>313.08</td>\n",
       "      <td>156.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:44:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107777</th>\n",
       "      <td>7977035</td>\n",
       "      <td>7</td>\n",
       "      <td>1260.93</td>\n",
       "      <td>95.26</td>\n",
       "      <td>363.78</td>\n",
       "      <td>180.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:45:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107778</th>\n",
       "      <td>7977079</td>\n",
       "      <td>7</td>\n",
       "      <td>1946.71</td>\n",
       "      <td>115.78</td>\n",
       "      <td>667.26</td>\n",
       "      <td>278.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:46:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107779</th>\n",
       "      <td>7977139</td>\n",
       "      <td>4</td>\n",
       "      <td>614.64</td>\n",
       "      <td>117.61</td>\n",
       "      <td>190.58</td>\n",
       "      <td>153.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:47:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107780</th>\n",
       "      <td>7977240</td>\n",
       "      <td>5</td>\n",
       "      <td>1353.65</td>\n",
       "      <td>148.17</td>\n",
       "      <td>523.76</td>\n",
       "      <td>270.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:48:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107781</th>\n",
       "      <td>7977288</td>\n",
       "      <td>5</td>\n",
       "      <td>609.24</td>\n",
       "      <td>65.85</td>\n",
       "      <td>156.07</td>\n",
       "      <td>121.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:49:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107782</th>\n",
       "      <td>7977397</td>\n",
       "      <td>4</td>\n",
       "      <td>742.25</td>\n",
       "      <td>115.08</td>\n",
       "      <td>271.17</td>\n",
       "      <td>185.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:50:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107783</th>\n",
       "      <td>7977427</td>\n",
       "      <td>11</td>\n",
       "      <td>2889.68</td>\n",
       "      <td>86.38</td>\n",
       "      <td>638.69</td>\n",
       "      <td>262.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:51:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107784</th>\n",
       "      <td>7977510</td>\n",
       "      <td>7</td>\n",
       "      <td>1295.70</td>\n",
       "      <td>94.63</td>\n",
       "      <td>392.86</td>\n",
       "      <td>185.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:52:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107785</th>\n",
       "      <td>7977572</td>\n",
       "      <td>8</td>\n",
       "      <td>1461.29</td>\n",
       "      <td>79.60</td>\n",
       "      <td>274.93</td>\n",
       "      <td>182.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:53:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107786</th>\n",
       "      <td>7977645</td>\n",
       "      <td>10</td>\n",
       "      <td>3208.71</td>\n",
       "      <td>91.62</td>\n",
       "      <td>1018.98</td>\n",
       "      <td>320.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:54:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107787</th>\n",
       "      <td>7977737</td>\n",
       "      <td>5</td>\n",
       "      <td>1450.59</td>\n",
       "      <td>88.01</td>\n",
       "      <td>883.06</td>\n",
       "      <td>290.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:55:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107788</th>\n",
       "      <td>7977831</td>\n",
       "      <td>6</td>\n",
       "      <td>1243.33</td>\n",
       "      <td>115.09</td>\n",
       "      <td>497.55</td>\n",
       "      <td>207.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:56:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107789</th>\n",
       "      <td>7977877</td>\n",
       "      <td>8</td>\n",
       "      <td>1636.18</td>\n",
       "      <td>91.81</td>\n",
       "      <td>567.30</td>\n",
       "      <td>204.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:57:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107790</th>\n",
       "      <td>7977935</td>\n",
       "      <td>8</td>\n",
       "      <td>1719.84</td>\n",
       "      <td>134.99</td>\n",
       "      <td>303.34</td>\n",
       "      <td>214.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:58:52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107791</th>\n",
       "      <td>7978043</td>\n",
       "      <td>7</td>\n",
       "      <td>1734.20</td>\n",
       "      <td>77.81</td>\n",
       "      <td>743.86</td>\n",
       "      <td>247.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2019-03-09 23:59:52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7601 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "100191  7434210      9       2750.62        108.90       1243.21   \n",
       "100192  7434303     10       2680.90        109.79        621.66   \n",
       "100193  7434351      3        375.49         69.80        158.44   \n",
       "100194  7434477      1        133.46        133.46        133.46   \n",
       "100195  7434554      4        431.09         76.27        130.19   \n",
       "100196  7434607      3        838.02        214.77        349.92   \n",
       "100197  7434641      2        348.50        109.39        239.11   \n",
       "100198  7434748      1         98.17         98.17         98.17   \n",
       "100199  7434769      2        320.97        150.27        170.70   \n",
       "100200  7434885      2        460.92        124.20        336.72   \n",
       "100201  7434925      5        778.83        114.60        218.03   \n",
       "100202  7434985      5       1187.60        148.29        483.77   \n",
       "100203  7435027      8       1327.02         64.50        494.33   \n",
       "100204  7435113      3        693.93        122.96        411.39   \n",
       "100205  7435146      1        222.28        222.28        222.28   \n",
       "100206  7435216      1        187.44        187.44        187.44   \n",
       "100207  7435317      1        150.75        150.75        150.75   \n",
       "100208  7435342      6       1023.25        104.30        327.92   \n",
       "100209  7435499      1        305.95        305.95        305.95   \n",
       "100210  7435518      3        435.64        125.27        168.81   \n",
       "100211  7435590      4       1013.84         80.00        693.16   \n",
       "100212  7435671      2        439.20        103.40        335.80   \n",
       "100213  7435754      1        101.32        101.32        101.32   \n",
       "100214  7435765      5        710.37         92.82        187.95   \n",
       "100215  7435822      6        893.37        103.36        219.25   \n",
       "100216  7435923      2        223.93        104.11        119.82   \n",
       "100217  7435990      1        200.50        200.50        200.50   \n",
       "100218  7436073      2        447.68        101.22        346.46   \n",
       "100219  7436124      4       1351.68        116.12        570.62   \n",
       "100220  7436217      2        336.21        165.75        170.46   \n",
       "...         ...    ...           ...           ...           ...   \n",
       "107762  7975946     12       2352.38         75.99        396.05   \n",
       "107763  7976014      8       1229.78         55.08        272.57   \n",
       "107764  7976098      5        783.28         71.80        287.61   \n",
       "107765  7976112     11       1692.83         72.39        340.03   \n",
       "107766  7976220     10       2142.50         88.62        336.89   \n",
       "107767  7976272     11       1696.39         66.28        299.57   \n",
       "107768  7976387      5        787.14         91.22        356.16   \n",
       "107769  7976439      9       1311.06         73.65        221.67   \n",
       "107770  7976528     11       1446.02         89.36        191.83   \n",
       "107771  7976580      9       1425.64         74.92        283.87   \n",
       "107772  7976654      6       1207.68         82.50        327.82   \n",
       "107773  7976734      5        858.62         82.31        280.92   \n",
       "107774  7976823      4        622.49         91.27        212.77   \n",
       "107775  7976860      5        992.69         75.42        353.22   \n",
       "107776  7976961      9       1411.06        102.67        313.08   \n",
       "107777  7977035      7       1260.93         95.26        363.78   \n",
       "107778  7977079      7       1946.71        115.78        667.26   \n",
       "107779  7977139      4        614.64        117.61        190.58   \n",
       "107780  7977240      5       1353.65        148.17        523.76   \n",
       "107781  7977288      5        609.24         65.85        156.07   \n",
       "107782  7977397      4        742.25        115.08        271.17   \n",
       "107783  7977427     11       2889.68         86.38        638.69   \n",
       "107784  7977510      7       1295.70         94.63        392.86   \n",
       "107785  7977572      8       1461.29         79.60        274.93   \n",
       "107786  7977645     10       3208.71         91.62       1018.98   \n",
       "107787  7977737      5       1450.59         88.01        883.06   \n",
       "107788  7977831      6       1243.33        115.09        497.55   \n",
       "107789  7977877      8       1636.18         91.81        567.30   \n",
       "107790  7977935      8       1719.84        134.99        303.34   \n",
       "107791  7978043      7       1734.20         77.81        743.86   \n",
       "\n",
       "        res_time_avg  interval           created_at  \n",
       "100191         305.0        60  2019-03-01 00:00:41  \n",
       "100192         268.0        60  2019-03-01 00:01:41  \n",
       "100193         125.0        60  2019-03-01 00:02:41  \n",
       "100194         133.0        60  2019-03-01 00:04:41  \n",
       "100195         107.0        60  2019-03-01 00:05:41  \n",
       "100196         279.0        60  2019-03-01 00:06:41  \n",
       "100197         174.0        60  2019-03-01 00:07:41  \n",
       "100198          98.0        60  2019-03-01 00:08:41  \n",
       "100199         160.0        60  2019-03-01 00:09:41  \n",
       "100200         230.0        60  2019-03-01 00:10:41  \n",
       "100201         155.0        60  2019-03-01 00:11:41  \n",
       "100202         237.0        60  2019-03-01 00:12:41  \n",
       "100203         165.0        60  2019-03-01 00:13:41  \n",
       "100204         231.0        60  2019-03-01 00:14:41  \n",
       "100205         222.0        60  2019-03-01 00:15:41  \n",
       "100206         187.0        60  2019-03-01 00:16:41  \n",
       "100207         150.0        60  2019-03-01 00:17:41  \n",
       "100208         170.0        60  2019-03-01 00:18:41  \n",
       "100209         305.0        60  2019-03-01 00:20:41  \n",
       "100210         145.0        60  2019-03-01 00:21:41  \n",
       "100211         253.0        60  2019-03-01 00:22:41  \n",
       "100212         219.0        60  2019-03-01 00:23:41  \n",
       "100213         101.0        60  2019-03-01 00:24:41  \n",
       "100214         142.0        60  2019-03-01 00:25:41  \n",
       "100215         148.0        60  2019-03-01 00:26:41  \n",
       "100216         111.0        60  2019-03-01 00:27:41  \n",
       "100217         200.0        60  2019-03-01 00:28:41  \n",
       "100218         223.0        60  2019-03-01 00:30:41  \n",
       "100219         337.0        60  2019-03-01 00:31:41  \n",
       "100220         168.0        60  2019-03-01 00:32:41  \n",
       "...              ...       ...                  ...  \n",
       "107762         196.0        60  2019-03-09 23:30:52  \n",
       "107763         153.0        60  2019-03-09 23:31:52  \n",
       "107764         156.0        60  2019-03-09 23:32:52  \n",
       "107765         153.0        60  2019-03-09 23:33:52  \n",
       "107766         214.0        60  2019-03-09 23:34:52  \n",
       "107767         154.0        60  2019-03-09 23:35:52  \n",
       "107768         157.0        60  2019-03-09 23:36:52  \n",
       "107769         145.0        60  2019-03-09 23:37:52  \n",
       "107770         131.0        60  2019-03-09 23:38:52  \n",
       "107771         158.0        60  2019-03-09 23:39:52  \n",
       "107772         201.0        60  2019-03-09 23:40:52  \n",
       "107773         171.0        60  2019-03-09 23:41:52  \n",
       "107774         155.0        60  2019-03-09 23:42:52  \n",
       "107775         198.0        60  2019-03-09 23:43:52  \n",
       "107776         156.0        60  2019-03-09 23:44:52  \n",
       "107777         180.0        60  2019-03-09 23:45:52  \n",
       "107778         278.0        60  2019-03-09 23:46:52  \n",
       "107779         153.0        60  2019-03-09 23:47:52  \n",
       "107780         270.0        60  2019-03-09 23:48:52  \n",
       "107781         121.0        60  2019-03-09 23:49:52  \n",
       "107782         185.0        60  2019-03-09 23:50:52  \n",
       "107783         262.0        60  2019-03-09 23:51:52  \n",
       "107784         185.0        60  2019-03-09 23:52:52  \n",
       "107785         182.0        60  2019-03-09 23:53:52  \n",
       "107786         320.0        60  2019-03-09 23:54:52  \n",
       "107787         290.0        60  2019-03-09 23:55:52  \n",
       "107788         207.0        60  2019-03-09 23:56:52  \n",
       "107789         204.0        60  2019-03-09 23:57:52  \n",
       "107790         214.0        60  2019-03-09 23:58:52  \n",
       "107791         247.0        60  2019-03-09 23:59:52  \n",
       "\n",
       "[7601 rows x 8 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用created_at作为时间索引\n",
    "df[(df.created_at >= '2019-03-01') & (df.created_at < '2019-03-10')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "2      162742      5        845.84        136.31        225.73         169.0   \n",
       "3      162808      9       1305.52         90.12        196.61         145.0   \n",
       "4      162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# interval这一列都一样，没什么分析价值，删除\n",
    "df = df.drop('interval', axis = 1)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20991695860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# api调用次数情况\n",
    "df['count'].hist(bins = 30)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 将时间的字符串类型转化为时间序列\n",
    "df.index = df['created_at']\n",
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Deghq9pDe3XzX1ya87vj9DWP3ZcROySmvIwf2DHjm4vK1+z7IeZ9oT70YQNSD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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2098bc83898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 切出一天的数据，绘制一天时段的接口调用情况\n",
    "df['2019-3-02']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 凌晨时间无人访问， 下午2，3点第一个访问高峰，晚上，8，9点，第二个访问高峰"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5nRSjnhUhwkWxwurX5etI8dxE223ObtO5rJ0uzMYeHkAISejP9jVgMuiYXRxY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\ng+Ey5f8B4gBueit5cwIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2098bd64748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 20分钟采样\n",
    "data = df['2019-5-1'].resample('20T').mean()\n",
    "data[['res_time_sum',\t'res_time_min',\t'res_time_max',\t'res_time_avg']].plot()\n",
    "plt.show()\n",
    "# 根据下表可以看出,业务高峰时段 下午2-3点，晚上7-8点，响应时间都是上升的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iKEqB8f8BWWaYLNQ45l8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x209916c9b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 连续的几天数据，可以发现，每天的业务高峰时段都比较相似 \n",
    "df['2019-5-1' : '2019-5-10']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([3, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "            ...\n",
       "            3, 3, 3, 3, 3, 3, 3, 3, 3, 3],\n",
       "           dtype='int64', name='created_at', length=865)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 0 代表星期一，  1 代表星期二 ，  5，6分别代表周六和周日\n",
    "df['2019-5-2'].index.weekday "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "\n",
       "                     res_time_max  res_time_avg           created_at  weekday  \n",
       "created_at                                                                     \n",
       "2018-11-01 00:00:07        177.72         132.0  2018-11-01 00:00:07        3  \n",
       "2018-11-01 00:01:07        240.38         149.0  2018-11-01 00:01:07        3  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekday'] = df.index.weekday\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:00:07</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-11-01 00:00:07  2019162542      8       1057.31         88.75   \n",
       "2018-11-01 00:01:07      162644      5        749.12        103.79   \n",
       "2018-11-01 00:02:07      162742      5        845.84        136.31   \n",
       "2018-11-01 00:03:07      162808      9       1305.52         90.12   \n",
       "2018-11-01 00:04:07      162943      3        568.89        138.45   \n",
       "\n",
       "                     res_time_max  res_time_avg           created_at  weekday  \\\n",
       "created_at                                                                      \n",
       "2018-11-01 00:00:07        177.72         132.0  2018-11-01 00:00:07        3   \n",
       "2018-11-01 00:01:07        240.38         149.0  2018-11-01 00:01:07        3   \n",
       "2018-11-01 00:02:07        225.73         169.0  2018-11-01 00:02:07        3   \n",
       "2018-11-01 00:03:07        196.61         145.0  2018-11-01 00:03:07        3   \n",
       "2018-11-01 00:04:07        232.02         189.0  2018-11-01 00:04:07        3   \n",
       "\n",
       "                     weekend  \n",
       "created_at                    \n",
       "2018-11-01 00:00:07    False  \n",
       "2018-11-01 00:01:07    False  \n",
       "2018-11-01 00:02:07    False  \n",
       "2018-11-01 00:03:07    False  \n",
       "2018-11-01 00:04:07    False  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断是否是周末 ，是不是5，6\n",
    "df['weekend'] = df['weekday'].isin({5, 6})\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    7.016846\n",
       "True     7.574989\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对weekend 进行分组， 对count列 求平均值\n",
    "df.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend  created_at\n",
       "False    0              3.239120\n",
       "         1              1.668388\n",
       "         2              1.162551\n",
       "         3              1.086705\n",
       "         4              1.155556\n",
       "         5              1.136364\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.000000\n",
       "         9              1.080000\n",
       "         10             1.239011\n",
       "         11             2.031690\n",
       "         12             4.195845\n",
       "         13             6.668042\n",
       "         14             8.260503\n",
       "         15             8.934448\n",
       "         16             8.466504\n",
       "         17             6.784996\n",
       "         18             6.717731\n",
       "         19             8.655913\n",
       "         20            10.536496\n",
       "         21            10.846906\n",
       "         22             9.034164\n",
       "         23             5.946834\n",
       "True     0              3.467782\n",
       "         1              1.741849\n",
       "         2              1.161826\n",
       "         3              1.050000\n",
       "         4              1.076923\n",
       "         5              1.333333\n",
       "         6              1.000000\n",
       "         7              1.000000\n",
       "         8              1.071429\n",
       "         9              1.144928\n",
       "         10             1.254111\n",
       "         11             1.992958\n",
       "         12             4.031889\n",
       "         13             6.905772\n",
       "         14             8.851321\n",
       "         15             9.858422\n",
       "         16             9.420550\n",
       "         17             7.334743\n",
       "         18             7.342150\n",
       "         19             9.270430\n",
       "         20            11.173609\n",
       "         21            11.695043\n",
       "         22            10.419916\n",
       "         23             7.025452\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o69fyv6kyPDHFhZFJlpdpR8RuGtwz0JkLIxiDVoOMkWbMpN453RQ7aTS4ZyDd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\nfUUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2098bc00940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>weekend</th>\n",
       "      <th>False</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.239120</td>\n",
       "      <td>3.467782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.668388</td>\n",
       "      <td>1.741849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.162551</td>\n",
       "      <td>1.161826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.086705</td>\n",
       "      <td>1.050000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.155556</td>\n",
       "      <td>1.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.136364</td>\n",
       "      <td>1.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.071429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.080000</td>\n",
       "      <td>1.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.239011</td>\n",
       "      <td>1.254111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.031690</td>\n",
       "      <td>1.992958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.195845</td>\n",
       "      <td>4.031889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6.668042</td>\n",
       "      <td>6.905772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>8.260503</td>\n",
       "      <td>8.851321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>8.934448</td>\n",
       "      <td>9.858422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8.466504</td>\n",
       "      <td>9.420550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.784996</td>\n",
       "      <td>7.334743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.717731</td>\n",
       "      <td>7.342150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8.655913</td>\n",
       "      <td>9.270430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10.536496</td>\n",
       "      <td>11.173609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.846906</td>\n",
       "      <td>11.695043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9.034164</td>\n",
       "      <td>10.419916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.946834</td>\n",
       "      <td>7.025452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "weekend         False      True \n",
       "created_at                      \n",
       "0            3.239120   3.467782\n",
       "1            1.668388   1.741849\n",
       "2            1.162551   1.161826\n",
       "3            1.086705   1.050000\n",
       "4            1.155556   1.076923\n",
       "5            1.136364   1.333333\n",
       "6            1.000000   1.000000\n",
       "7            1.000000   1.000000\n",
       "8            1.000000   1.071429\n",
       "9            1.080000   1.144928\n",
       "10           1.239011   1.254111\n",
       "11           2.031690   1.992958\n",
       "12           4.195845   4.031889\n",
       "13           6.668042   6.905772\n",
       "14           8.260503   8.851321\n",
       "15           8.934448   9.858422\n",
       "16           8.466504   9.420550\n",
       "17           6.784996   7.334743\n",
       "18           6.717731   7.342150\n",
       "19           8.655913   9.270430\n",
       "20          10.536496  11.173609\n",
       "21          10.846906  11.695043\n",
       "22           9.034164  10.419916\n",
       "23           5.946834   7.025452"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean().unstack(level = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2sWDBAubOnYvN1vg/wCOPPMKvf/1rMjIyyMnJYdq0aezYsaPR44VwmdKTxvZng26oK0bV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2098bc000f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['weekend', df.index.hour])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
